Dynamic driving metric output generation using computer vision methods

ABSTRACT

Aspects of the disclosure relate to dynamic driving metric output platforms that utilize improved computer vision methods to determine vehicle metrics from video footage. A computing platform may receive video footage from a vehicle camera. The computing platform may determine that a reference marker in the video footage has reached a beginning and an end of a road marker based on brightness transitions, and may insert time stamps into the video accordingly. Based on the time stamps, the computing platform may determine an amount of time during which the reference marker covered the road marking. Based on a known length of the road marking and the amount of time during which the reference marker covered the road marking, the computing platform may determine a vehicle speed. The computing platform may generate driving metric output information, based on the vehicle speed, which may be displayed by an accident analysis platform. Based on known dimensions of pavement markings the computing platform may obtain the parameters of the camera (e.g., focal length, camera height above ground plane and camera tilt angle) used to generate the video footage and use the camera parameters to determine the distance between the camera and any object in the video footage.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. Ser. No. 16/142,696filed on Sep. 26, 2018, and entitled “Dynamic Driving Metric OutputGeneration Using Computer Vision Methods,” which is hereby incorporatedby reference as to its entirety.

BACKGROUND

Aspects of the disclosure relate to enhanced processing systems forproviding dynamic driving metric outputs using improved computer visionmethods. In particular, one or more aspects of the disclosure relate todynamic driving metric output platforms that utilize video footage tocompute driving metrics.

Many organizations and individuals rely on vehicle metrics such as speedand acceleration to perform driving and/or accident evaluations. In manyinstances, however, a vehicle may be equipped with an array of sensors,which include cameras and sensors such as LIDAR and radar, and thus allthe speeds and distances are obtained from these sensors. This situationmay present limitations to those without access to a fully equippedvehicle with cameras, LIDAR and radar that can capture the importantdistance, speed and acceleration metrics. There remains an ever-presentneed to develop alternative solutions to calculate vehicle metrics.

SUMMARY

Aspects of the disclosure provide effective, efficient, scalable, andconvenient technical solutions that address and overcome the technicalproblems associated with determining driving metrics from video capturedby a vehicle camera by implementing advanced computer vision methods anddynamic driving metric output generation. In accordance with one or morearrangements discussed herein, a computing platform having at least oneprocessor, a communication interface, and memory may receive videofootage from a vehicle camera. The computing platform may determine thata reference marker in the video footage has reached a beginning of aroad marking by determining that a first brightness transition in thevideo footage exceeds a predetermined threshold. In response todetermining that the reference marker has reached the beginning of theroad marking, the computing platform may insert, into the video footage,a first time stamp indicating a time at which the reference markerreached the beginning of the road marking. The computing platform maydetermine that the reference marker has reached an end of the roadmarking by determining that a second brightness transition in the videofootage exceeds the predetermined threshold. In response to determiningthat the reference marker has reached the end of the road marking, thecomputing platform may insert, into the video footage, a second timestamp indicating a time at which the reference marker reached the end ofthe road marking. Based on the first time stamp and the second timestamp, the computing platform may determine an amount of time duringwhich the reference marker covered the road marking. Based on a knownlength of the road marking and the amount of time during which thereference marker covered the road marking, the computing platform maydetermine a vehicle speed. Based on the vehicle speed, the computingplatform may generate driving metric output information. The computingplatform may generate one or more commands directing an accidentanalysis platform to generate and cause display of a driving metricinterface based on the driving metric output information. The computingplatform may establish a first wireless data connection with theaccident analysis platform. While the first wireless data connection isestablished, the computing platform may send, to the accident analysisplatform, the driving metric output information and the one or morecommands directing the accident analysis platform to generate and causedisplay of the driving metric interface based on the driving metricoutput information. In some arrangements, the computing platform mayestablish a second wireless data connection with a vehicle camera,wherein the video footage is received while the second wireless dataconnection is established.

In some arrangements, the computing platform may establish a secondwireless data connection with a vehicle camera, wherein the videofootage is received while the second wireless data connection isestablished. In some arrangements, the computing platform may cause thecomputing platform to determine that the video footage contains a roadmarking associated with a standard length.

In some arrangements, the computing platform may insert, into the videofootage, a reference marker, wherein the reference marker corresponds toa fixed position in the video footage. In some arrangements, thecomputing platform may generate one or more commands directing a vehicleattribute database to provide vehicle parameters for a vehiclecorresponding to the vehicle camera. The computing platform mayestablish a third wireless data connection with the vehicle attributedatabase. While the third wireless data connection is established, thecomputing platform may send the one or more commands directing thevehicle attribute database to provide the vehicle parameters.

In some arrangements, the computing platform may receive a vehicleparameter output corresponding to the vehicle parameters. Based on thevehicle parameters and the distance between the vehicle camera and anobject in the video footage, the computing platform may determine adistance between the vehicle and the object in the video footage.

In some arrangements, the computing platform may update the drivingmetric output information based on the distance between the vehicle andthe object in the video footage. The computing platform may generate oneor more commands directing the accident analysis platform to generateand cause display of an updated driving metric interface based on theupdated driving metric output information. While the first wireless dataconnection is established, the computing platform may send, to theaccident analysis platform, the updated driving metric outputinformation and the one or more commands directing the accident analysisplatform to generate and cause display of the updated driving metricinterface based on the updated driving metric output information.

These features, along with many others, are discussed in greater detailbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example and not limitedin the accompanying figures in which like reference numerals indicatesimilar elements and in which:

FIGS. 1A-1B depict an illustrative computing environment for deploying adynamic driving metric output platform that utilizes improved computervision methods in accordance with one or more example arrangementsdiscussed herein;

FIGS. 2A-2J depict an illustrative event sequence deploying a dynamicdriving metric output platform that utilizes improved computer visionmethods in accordance with one or more example arrangements discussedherein;

FIGS. 3A-3B depict the use of additional pavement markings by a dynamicdriving metric output platform that utilizes improved computer visionmethods in accordance with one or more example arrangements discussedherein;

FIG. 4 depicts a brightness transition along a road marking determinedby a dynamic driving metric output platform that utilizes improvedcomputer vision methods in accordance with one or more examplearrangements discussed herein;

FIG. 5 depicts three image frames used to estimate absolute ego-vehiclespeed by a dynamic driving metric output platform that utilizes improvedcomputer vision methods in accordance with one or more examplearrangements discussed herein;

FIG. 6 depicts a graphical user interface for a dynamic driving metricoutput platform that utilizes improved computer vision methods inaccordance with one or more example arrangements discussed herein;

FIGS. 7-12 depict sketches for a dynamic driving metric output platformthat utilizes improved computer vision methods in accordance with one ormore example arrangements discussed herein;

FIG. 13 depicts a graphical user interface for a dynamic driving metricoutput platform that utilizes improved computer vision methods inaccordance with one or more example arrangements discussed herein;

FIGS. 14 and 15 depict sketches for a dynamic driving metric outputplatform that utilizes improved computer vision methods in accordancewith one or more example arrangements discussed herein;

FIG. 16 depicts a graphical user interface for a dynamic driving metricoutput platform that utilizes improved computer vision methods inaccordance with one or more example arrangements discussed herein;

FIG. 17 depicts a sketch for a dynamic driving metric output platformthat utilizes improved computer vision methods in accordance with one ormore example arrangements discussed herein; and

FIG. 18 depicts an illustrative method for deploying a dynamic drivingmetric output platform that utilizes improved computer vision methods inaccordance with one or more example arrangements discussed herein.

DETAILED DESCRIPTION

In the following description of various illustrative embodiments,reference is made to the accompanying drawings, which form a parthereof, and in which is shown, by way of illustration, variousembodiments in which aspects of the disclosure may be practiced. It isto be understood that other embodiments may be utilized, and structuraland functional modifications may be made, without departing from thescope of the present disclosure.

It is noted that various connections between elements are discussed inthe following description. It is noted that these connections aregeneral and, unless specified otherwise, may be direct or indirect,wired or wireless, and that the specification is not intended to belimiting in this respect.

One of the current limitations faced by researchers in the autonomousvehicle industry is the fact that the models they develop for riskprediction, driver behavior prediction, driver frustration estimation,and others may require metrics related to the driving scenario, whichmight not be readily available on the multitude of driving videodatasets available on the Internet. Among the critical metrics thatcharacterize any driving scenario there are the absolute speed of thedriver's vehicle (which may be called the ego-vehicle), the relativespeed of neighbor vehicles, the distance from the ego-vehicle to all thesurrounding vehicles, the acceleration of the ego-vehicle, and theaccelerations of all the neighbor vehicles. These metrics may define themechanics of the interactions among the agents involved on the scenarioand thus may be of the utmost importance for modeling, estimation andprediction. All the metrics mentioned may be obtained and derived fromthe absolute speed of the ego-vehicle and from the distance between theego vehicle to all the neighbor vehicles.

What is common practice today in industry is to equip a vehicle with anarray of sensors which include cameras and sensors such as LIDAR andradar, and thus all the speeds and distances are obtained from thesesensors. This situation presents limitations to researchers in academiaand industry because not all of them may have the ability and thefunding to get access to a fully equipped vehicle with cameras, LIDARand radar that can capture the important distance, speed andacceleration metrics. For the cases where the researcher can use a fullyequipped vehicle, the amount of information that may be collected comesat a cost since it may take time to perform the drive-runs, which isanother limitation.

The present disclosure provides a method to overcome these limitationsby allowing the extraction of the driving scenario's principal metricsfrom the existing datasets of recorded driving video. The describedmethod offers the advantage that the metrics may be extracted from thevideo data only, without any other sensor data such as LIDAR, or radar.Therefore, just the driving video may be sufficient.

One of the roadblocks that prevents the ability to extract thisinformation is the fact that for video coming from existing datasets theinformation about the camera parameters that were used to record themultitude of videos might not be available. In this case, criticalcamera parameter information may be the focal length and the heightabove the ground plane at which the camera was mounted. The presentdisclosure provides a method that may allow determination of speed anddistances without a priori knowledge of these camera parameters.Therefore, this may unlock many possibilities since there is a greatdeal of driving data available that may be tapped not only to validatemodels but also to train them. Additionally, the method described allowsthe real-time deployment of models for prediction and estimation at lowcost and with simplicity since the input data for the distance, speed,and acceleration metrics comes only from the camera and does notimplement LIDAR and radar equipment.

FIGS. 1A-1B depict an illustrative computing environment for deploying adynamic driving metric output platform 102 that utilizes improvedcomputer vision methods in accordance with one or more exampleembodiments. Referring to FIG. 1A, computing environment 100 may includeone or more computer systems. For example, computing environment 100 mayinclude a dynamic driving metric output platform 102, a video camera103, a vehicle attribute database 104, and an accident analysis platform105.

As illustrated in greater detail below, dynamic driving metric outputplatform 102 may include one or more computing devices configured toperform one or more of the functions described herein. For example,dynamic driving metric output platform 102 may include one or morecomputers (e.g., laptop computers, desktop computers, servers, serverblades, or the like) configured to receive video footage and generatedriving metric outputs based on the video.

In addition, and as illustrated in greater detail below, the dynamicdriving metric output platform 102 may be configured to generate, host,transmit, and/or otherwise provide one or more web pages and/or othergraphical user interfaces (which may, e.g., cause one or more othercomputer systems to display and/or otherwise present the one or more webpages and/or other graphical user interfaces). In some instances, theweb pages and/or other graphical user interfaces generated by dynamicdriving metric output platform 102 may be associated with an externalportal provided by an organization, such as an accident analysis portalprovided by an insurance institution or provider.

Video camera 103 may be a vehicle camera or a camera on a personalcomputing device (e.g., smartphone, personal digital assistant, tabletcomputer, or the like). In some instances, the video camera 103 may be aforward facing camera, a rear facing camera, and or a side facingcamera. In some instances, there may be more than one camera. Forexample, video footage may be captured using a dash board vehicle cameraand a camera on a mobile device. In some instances, the video camera 103may be mounted in the vehicle and may rotate automatically or manually.Although computing environment 100 as shown includes a single videocamera 103, it should be understood that the computing environment 100may include any number of video cameras similar to video camera 103. Insome instances, the video camera may be mounted in the vehicle above thedashboard so that the camera has visibility to a scene in front of thevehicle.

Vehicle attribute database 104 may be a computing platform capable ofstoring and maintaining attributes (e.g., operating parameters,dimensions, or the like) corresponding to various vehicles. In someinstances, the vehicle attribute database 104 may be configured toreceive requests for vehicle attributes from the dynamic driving metricoutput platform, determine the corresponding attributes, and send theattributes to the dynamic driving metric output platform 102.

Accident analysis platform 105 may be a computing device (e.g., adesktop computer, laptop computer, tablet computer, smart phone, or thelike) that may be used to receive driving metric outputs from thedynamic driving metric output platform 102 and to cause display of thedriving metrics outputs. For example, the accident analysis platform 105may be used by an employee of an insurance institution to analyzedriving quality and/or to perform accident evaluation. Accident AnalysisPlatform may run on-board the vehicle and the results of the modelsprocessed by this platform can be available to the driver on boardwithout requiring a network connection.

In addition, and as illustrated in greater detail below, the accidentanalysis platform 105 may be configured to generate, host, transmit,and/or otherwise provide one or more web pages and/or other graphicaluser interfaces (which may, e.g., cause one or more other computersystems to display and/or otherwise present the one or more web pagesand/or other graphical user interfaces). In some instances, the webpages and/or other graphical user interfaces generated by accidentanalysis platform 105 may be associated with an external portal providedby an organization, such as an accident analysis portal provided by aninsurance institution or provider.

Computing environment 100 also may include one or more networks, whichmay interconnect one or more of dynamic driving metric output platform102, video camera 103, vehicle attribute database 104, accident analysisplatform 105. For example, computing environment 100 may include anetwork 101 (which may, e.g., interconnect dynamic driving metric outputplatform 102, video camera 103, vehicle attribute database 104, andaccident analysis platform 105).

In one or more arrangements, dynamic driving metric output platform 102,video camera 103, vehicle attribute database 104, accident analysisplatform 105, and/or the other systems included in computing environment100 may be any type of computing device capable of receiving a userinterface, receiving input using the user interface, and communicatingthe received input to one or more other computing devices. For example,dynamic driving metric output platform 102, video camera 103, vehicleattribute database 104, accident analysis platform 105, and/or the othersystems included in computing environment 100 may, in some instances, beand/or include server computers, desktop computers, laptop computers,tablet computers, smart phones, or the like that may include one or moreprocessors, memories, communication interfaces, storage devices, and/orother components. As noted above, and as illustrated in greater detailbelow, any and/or all of dynamic driving metric output platform 102,video camera 103, vehicle attribute database 104, and accident analysisplatform 105 may, in some instances, be special-purpose computingdevices configured to perform specific functions.

Referring to FIG. 1B, dynamic driving metric output platform 102 mayinclude one or more processors 111, memory 112, and communicationinterface 113. A data bus may interconnect processor 111, memory 112,and communication interface 113. Communication interface 113 may be anetwork interface configured to support communication between dynamicdriving metric output platform 102 and one or more networks (e.g.,network 101, or the like). Memory 112 may include one or more programmodules having instructions that when executed by processor 111 causedynamic driving metric output platform 102 to perform one or morefunctions described herein and/or one or more databases that may storeand/or otherwise maintain information which may be used by such programmodules and/or processor 111. In some instances, the one or more programmodules and/or databases may be stored by and/or maintained in differentmemory units of dynamic driving metric output platform 102 and/or bydifferent computing devices that may form and/or otherwise make updynamic driving metric output platform 102. For example, memory 112 mayhave, store, and/or include a dynamic driving metric output module 112a, a dynamic driving metric output database 112 b, and a machinelearning engine 112 c. Dynamic driving metric output module 112 a mayhave instructions that direct and/or cause dynamic driving metric outputplatform 102 to execute advanced computer vision methods for determiningdriving metric outputs, as discussed in greater detail below. Dynamicdriving metric output database 112 b may store information used bydynamic driving metric output module 112 a and/or dynamic driving metricoutput platform 102 in dynamic driving metric output generation and/orin performing other functions. Machine learning engine 112 c may haveinstructions that direct and/or cause the dynamic driving metric outputplatform 102 to perform dynamic driving metric output generation and toset, define, and/or iteratively refine optimization rules and/or otherparameters used by the dynamic driving metric output platform 102 and/orother systems in computing environment 100.

FIGS. 2A-2J depict an illustrative event sequence for deploying adynamic driving metric output platform 102 that utilizes improvedcomputer vision methods to determine driving metrics in accordance withone or more example embodiments. This illustrative event sequencedepicts a method for estimating ego-vehicle speed based on the abilityto detect pavement road markings and uses the pre-established dimensionsand spacing between these markings. In the case of US highways, urban,suburban and rural roads, the length of white road markings (e.g.,broken lines between lanes of traffic going in a same direction) oryellow road markings (e.g., broken lines between lanes of traffic goingin opposite directions) is universally established at ten feet.Similarly, the spacing between one white road marking (or yellow roadmarking) and the next is thirty feet. Given the fact that the white roadmarkings (and/or yellow road markings) with the dimensions mentionedabove are ubiquitous on US roads, this information may be exploited andused as a reference to determine the time it takes for the ego-vehicleto go from one white road marking to the next. Given the known distancebetween the markings, knowing the time to move over such distance mayprovide the speed. For simplicity, aspects herein may be described withrespect to use of white markings, but it should be understood thatyellow markings may be used without departing from the scope of thedisclosure.

Referring to FIG. 2A, at step 201, the dynamic driving metric outputplatform 102 may establish a connection with the video camera 103. Forexample, the dynamic driving metric output platform may establish afirst wireless data connection to the dynamic driving metric outputplatform 102 to link the dynamic driving metric output platform 102 tothe video camera 103.

At step 202 the dynamic driving metric output platform 102 may generateone or more commands directing the video camera 103 to initiate videocollection. At step 203, the dynamic driving metric output platform 102may send the one or more commands directing the video camera 103 toinitiate video collection to the video camera 103. In some instances,the dynamic driving metric output platform 102 may send the one or morecommands directing the video camera 103 to initiate video collection viathe communication interface 113 and while the first wireless dataconnection is established.

At step 204, the video camera 103 may receive the one or more commandsdirecting the video camera 103 to initiate video collection sent at step203. In some instances, the video camera 103 may receive the one or morecommands directing the video camera 103 to initiate video collectionwhile the first wireless data connection is established.

At step 205, the video camera 103 may capture video footage. In someinstances, the video camera 103 may be located above a vehicledashboard, and may capture video footage from the viewing angle of adriver of the vehicle (e.g., a road in front of the vehicle).

Referring to FIG. 2B, at step 206, the video camera 103 may send thevideo footage to the dynamic driving metric output platform 102. Forexample, the video camera 103 may send the video footage to the dynamicdriving metric output platform 102 while the first wireless dataconnection is established.

At step 207, the dynamic driving metric output platform 102 may receivethe video footage. For example, the dynamic driving metric outputplatform 102 may receive the video footage while the first wireless dataconnection is established and via the communication interface 113.

At step 208, the dynamic driving metric output platform 102 may performactual detection of the white road markings on each of the 2D imageframes of the video footage received at step 207, which may be achievedthrough any methods available for lane detection. Lanes based on thewhite road markings may be located to the left and/or to the right ofthe actual trajectory of the ego-vehicle. In some instances, a group ofconsecutive white road markings may define the lane. In some instances,other lanes beyond the ones mentioned may be detected. However, for thepurposes of speed estimation, it may be sufficient to detect just one ofthe lanes in the immediate vicinity of the car.

In detecting the road markings, the dynamic driving metric outputplatform 102 may determine the parameters of a 2D line (e.g., line slopeand a 2D point belonging to the line) that represents the actual laneobtained by finding one single line that goes through the middle of allthe white road markings. In some instances, this line may be referred toas the 2D line that represents the lane.

In some instances, white road markings might not be available in thescene. In these instances, the dynamic driving metric output platform102 may rely on other pavement markings that have predefined dimensions.FIGS. 3A-3B below provides two examples of these other markings. Thedimension of the arrow 305 that is usually found very frequently onstreet roads on urban, suburban and rural roads is nine feet as FIG. 3Ashows. The dynamic driving metric output platform 102 may also use thewords written in the pavement such as the “ONLY” word 310 that has apredefined height of eight feet as shown in FIG. 3B. There are otherpavement markings besides the two ones mentioned that may also be usedto estimate camera parameters (for example the STOP AHEAD words areseparated by forty feet and the words have a height of eight feet).Thus, there are several ways to extract the camera parameters fordistance estimation.

At step 209, the dynamic driving metric output platform 102 mayestablish a reference marker (a 2D marker) on the image frame toestablish timing when going from one image frame to the next. In someinstances, the marker may be an imaginary short horizontal line locatedat a mostly fixed position on the image frame. By inserting thereference marker, the dynamic driving metric output platform 102 mayestablish a fixed position on the image frame that may be reasonablyconsidered as a fixed position on the ground plane (road plane) that islocated in front of the ego-vehicle (this fixed position on the groundplane is always at a fixed distance in front of the ego-vehicle and thusin the real 3D world it moves together with the ego-vehicle). Byestablishing the fixed position, the dynamic driving metric outputplatform 102 may determine a position corresponding to some fixed amountof meters in front of the car. In some instances, as the vehicle movesforward “X” feet/meters, this fixed position over the ground plane mayalso move forward “X” feet/meters. In some instances, the dynamicdriving metric output platform 102 may use this fixed position toestimate the distance traveled by the vehicle because the fixed positionand the vehicle may move the same distances. In these instances, theremaining task is to determine for a given distance that the markermoves what is the time that elapsed.

In some instances, the 2D marker line may intercept the 2D linerepresenting the lane, and since the lane is mostly at a stable positionbetween frames (because cars usually go through stable trajectories thatallow the lanes to be located at the same positions across frames exceptfor lane changes), then by fixing the x-coordinate of the marker'scentroid the dynamic driving metric output platform 102 may ensure anintersection between the 2D line representing the lane and the markeracross frames. In some instances, the y-coordinate of the marker mightnot be critical, however it is preferred to set it on the lower portionof the image frame. In some instances, the dynamic driving metric outputplatform 102 may determine a lane change by the ego-vehicle and uponcompletion of the lane change the dynamic driving metric output platform102 may re-position the marker to maintain the intersection between themarker and the stable 2D line representing the lane.

At step 210, the dynamic driving metric output platform 102 may convertthe image frame to a grey scale image. In some instances, the dynamicdriving metric output platform 102 may convert the image frame to a greyscale image using the location of the marker inserted at step 209.

Referring to FIG. 2C, at step 211, the dynamic driving metric outputplatform 102 may detect, over the gray scale image, a high relativetransitioning on the brightness across the length of the marker (e.g.,brightness transition level exceeds a predetermined threshold). In doingso, the dynamic driving metric output platform 102 may detect the momentwhen the white road marking touches the marker. In some instances, thedynamic driving metric output platform 102 may start the speedestimation method on an image frame where the marker is located over theactual road pavement far away from any white road marking. In theseinstances, the dynamic driving metric output platform 102 may determinea uniform measure of brightness across the length of the marker becausethe brightness of the road pavement may be mostly uniform. This isfurther illustrated by FIG. 5 that shows the sketches of white roadmarkings and it shows the 2D marker position at three points in time(each point in time is represented by one of the three image frames).The frame to the left (frame 505) illustrates the example describedabove.

The dynamic driving metric output platform 102 may continue to progressthrough the images that make up the video footage. In doing so, thedynamic driving metric output platform 102 may determine that a whiteroad marking is closer to the marker's position (due to the forwardmotion of the ego-vehicle) in each image. As a result, at some point thedynamic driving metric output platform 102 may determine that the markeris located over the beginning of the white road marking (FIG. 5 centralframe illustrates this). In this instance, the dynamic driving metricoutput platform 102 may determine that the marker touches the white roadmarking. In making this determination, the dynamic driving metric outputplatform 102 may detect a relatively high transition between thebrightness of the image frame pixels when going from left to right oneach of the marker's individual point locations. In some instances, thedynamic driving metric output platform 102 may determine the transitionbecause of the contrast that exists between the brightness of thepavement and the brightness of the white marking. In these instances,the marker may be long enough so that the marker is longer than thewidth of the white road marking. As a result, as the dynamic drivingmetric output platform 102 reads pixels from left to right, the firstpixels read may be located over the pavement and a transition may occuron a white marking. The dynamic driving metric output platform 102 mayset a threshold for detection by making the brightness of the white roadmarking a multiplicative factor higher than the brightness of thesurrounding road pavement. In some instances, overall luminosityvariations between image frames might not influence the accuracy of thedetection because these variations increase or decrease brightnesslevels of pavement and white marking equally and thus the relativebrightness stays mostly unmodified. FIG. 4 illustrates a typical case ofthe marker 405 touching the beginning of a white road marking and thecorresponding three zones for brightness that result from reading pixelbrightness along the marker from left to right (two low brightness zonescorresponding to the pavement and a central high brightness zonecorresponding to the white road mark 410). In some instances, it may beenough for the dynamic driving metric output platform 102 to detect thefirst high brightness transition indicated on the figure, however asecond transition from high to low brightness may also be detected bythe dynamic driving metric output platform 102 to confirm the presenceof the white road marking.

At step 212, the dynamic driving metric output platform 102 maydetermine that the white road marking touches the marker and may set thefirst time-stamp by recording the image frame index number. In someinstances, the dynamic driving metric output platform 102 might notdetermine locations of all the four corners of the white road markingthrough the method described herein. In these instances, determining aline's slope and one 2D point belonging to the representative line maybe sufficient to produce the precise location for such line. Thus, thisdisclosure describes a method to detect the moment when the beginning ofthe white road marking touches the marker.

After inserting the first time-stamp, the dynamic driving metric outputplatform 102 may move over the next frames and the marker may continueto detect more high brightness transitions. This will continue until themoment when we reach the end of the white road marking.

At step 213, the dynamic driving metric output platform 102 maydetermine the end of the white road marking. In some instances, thedynamic driving metric output platform 102 may determine the end of thewhite road marking by detecting an absence of high transition after thestreak of high transitions that started with the first time-stamp (e.g.,brightness transition level exceeds a predetermined threshold). Thisevent may signal that the marker reached the end of the white roadmarking. At the frame when this happens, the dynamic driving metricoutput platform 102 may record the image frame index number, which maybecome the second time-stamp.

This method is further illustrated with regards to FIG. 5. FIG. 5 belowillustrates the procedure mentioned above by showing sketchescorresponding to three image frames. The sketches show the white roadmarkings that may correspond to the lane that may be detected over theleft side of the vehicle. Frame 505 shows two consecutive white roadmarkings A and B and the 2D marker location (which is close to thebeginning of white road marking A), the index number for this fame is:X. Frame 510 illustrates the moment when the beginning of the white roadmarking touches the marker. The center frame's index number is: X+s andthis number becomes the first time-stamp. Frame 515 shows the momentwhen the marker reaches the end of white road marking A (the sketch alsoshows the next white road marking C that would come after B). The indexnumber for frame 515 is: X+s+v and this number becomes the secondtime-stamp.

At step 214, the dynamic driving metric output platform 102 maydetermine the time in seconds that elapsed between the first and secondtime stamps, which may ultimately be used to determine speed. In someinstances, the dynamic driving metric output platform 102 may use thevideo's frame rate measured in frames per seconds (fps). This is a valuethat may universally be available on the meta data in the video file forall video formats. The dynamic driving metric output platform 102 maydetermine the time in seconds between time stamps using equation onebelow:

$\begin{matrix}{{{Time\_ between}{\_ time}{\_ stamps}} = \frac{v}{fr}} & (1)\end{matrix}$

Referring to FIG. 2D, at step 215, the dynamic driving metric outputplatform 102 may calculate a vehicle speed. In equation one above fr maybe the frame rate in fps. Therefore, the dynamic driving metric outputplatform 102 may determine the speed in feet/seconds by dividing thedistance of ten feet (distance between time stamps one and two) by thetime expressed in equation one. Thus the speed of the ego-vehicle forthe case described above is calculated by the dynamic driving metricoutput platform using equation two:

$\begin{matrix}{{Speed} = \frac{10 \times {fr}}{v}} & (2)\end{matrix}$

For a typical video file at thirty fps the maximum speed that may bedetected with the current method is two hundred miles/hr, which is farabove the range of speeds that is allowed on all US roads.

It should be noted that even through a lane change the dynamic drivingmetric output platform 102 may detect the speed using the methodmentioned above with the caveat that the x-coordinate of the marker'scentroid may move as the lane moves. The dynamic driving metric outputplatform 102 may still calculate the timings for the time-stamps throughthe same procedure described above, although the x-coordinate of themarker's centroid positioning that follows the lane through the lanechange maneuver may change.

In some instances, this may allow the dynamic driving metric outputplatform 102 to use one single white road marking to determine a speedof the vehicle. In some instances, the dynamic driving metric outputplatform 102 may use two consecutive white road markings. In thisinstance, after the second time-stamp mentioned above, the dynamicdriving metric output platform 102 may set a third time-stamp byrecording the frame index number when the marker touches the beginningof white road marking B (as shown in FIG. 5), which is at a distance ofthirty feet from the end of white road marking A (as shown in FIG. 5).In this instance, the dynamic driving metric output platform 102 may usethe third and the second time-stamps to determine the time for theego-vehicle to travel thirty feet. After this point, the dynamic drivingmetric output platform 102 may return to step 212, and may assign thethird time-stamp as the first time-stamp before initiating a new cycleof speed estimation.

In yet another instance, the dynamic driving metric output platform 102may use two lanes instead of one to determine the vehicle speed. In thisinstance, in addition to the left lane as it was the case illustratedabove, the dynamic driving metric output platform 102 may also use theright lane and thus may incorporate another set of white road markingsinto the estimation process. For example, the dynamic driving metricoutput platform 102 may estimate speed using the left lane. In thisexample, since the white road markings on a typical left-lane and rightlane scenario are not aligned (at a point when we have pavement on theleft lane we have white road marking on the right lane) the dynamicdriving metric output platform 102 may determine an additionalego-vehicle speed reading. For example, the dynamic driving metricoutput platform may estimate an additional speed reading usinginformation from the right lane (with the marker assigned to the rightlane having touched already the beginning of a white road marking) whilethe marker on the left lane is still between the end of a white roadmarking and the beginning of the next.

It should be noted that there may be a little quantization errorassociated with this speed determination method due to the fact that thenumber of frames per second may be finite. In this case, it may bepossible for the speed marker not to “touch” the actual beginning of thewhite road marking. Instead, the first time the marker lands on thewhite road marking it may land a little bit beyond the beginning. Inthis instance, the distance between time-stamps one and two may be lessthan ten feet. This may be particularly true for high ego-vehiclespeeds. The dynamic driving metric output platform 102 may solve thissituation at the frame where the marker lands on the white marking byperforming a scan along the line representing the lane and performingthe same type of brightness transition detection mentioned above overhorizontal lines that intercept the lane. The horizontal lines forscanning may move below the position (e.g., in the direction ofincreasing the y-coordinate) where the marker landed to search for theactual beginning of the white road marking. The beginning of the whiteroad marking may be detected at the first time an absence of a highbrightness transition occurs, which signals that the dynamic drivingmetric output platform 102 has reached the pavement. Thus, the dynamicdriving metric output platform 102 may register the y-locationimmediately above the absence detected above as the beginning of thewhite marking. The dynamic driving metric output platform 102 mayresolve the quantization error for the case of reaching the end of thewhite road marking by applying a scanning procedure similar to the onementioned above, with the difference being that instead of scanningbelow the marker, the dynamic driving metric output platform 102 mayscan above the marker searching for the actual end of the white roadmarking.

At step 216, the dynamic driving metric output platform 102 may generatedriving metric output information and one or more commands directing theaccident analysis platform 105 to generate a driving metric outputinterface using the driving metric output information. In someinstances, in generating the driving metric output information, thedynamic driving metric output platform 102 may generate informationcorresponding to the speed of the vehicle determined at step 215.

At step 217, the dynamic driving metric output platform 102 mayestablish a connection with accident analysis platform 105. In someinstances, the dynamic driving metric output platform 102 may establisha second wireless data connection with the accident analysis platform105 to link the dynamic driving metric output platform 102 to theaccident analysis platform 105.

At step 218, the dynamic driving metric output platform 102 may send thedriving metric output information and the one or more commands directingthe accident analysis platform 105 to generate the driving metric outputinterface using the driving metric output information to the accidentanalysis platform 105. In some instances, the dynamic driving metricoutput platform 102 may send the driving metric output information andthe one or more commands directing the accident analysis platform 105 togenerate the driving metric output interface using the driving metricoutput information via the communication interface and while the secondwireless data connection is established.

At step 219, the accident analysis platform 105 may receive the drivingmetric output information and the one or more commands directing theaccident analysis platform 105 to generate the driving metric outputinterface using the driving metric output information from the dynamicdriving metric output platform 102. In some instances, the accidentanalysis platform 105 may receive the driving metric output informationand the one or more commands directing the accident analysis platform105 to generate the driving metric output interface using the drivingmetric output information while the second wireless data connection isestablished.

Referring to FIG. 2E, at step 220, the accident analysis platform 105may generate a driving metric output interface based on the drivingmetric output information and the one or more commands directing theaccident analysis platform 105 to generate the driving metric outputinterface using the driving metric output information received at step219. In some instances, in generating the driving metric outputinterface, the accident analysis platform 105 may generate and causedisplay of a graphical user interface similar to graphical userinterface 605, which is shown in FIG. 6. In some instances, in causingdisplay of the graphical user interface 605, the accident analysisplatform 105 may cause display of various driving metrics determinedthroughout the method, such as the speed determined at step 215 and/oran acceleration value.

In addition to, or as an alternative to, estimating the vehicle speed asdescribed above, the dynamic driving metric output platform 102 mayestimate distances. In some instances, the dynamic driving metric outputplatform 102 may estimate distance based on perspective projectionmethods and more specifically based on perspective divide calculations.In performing the perspective divide calculations, the dynamic drivingmetric output platform 102 may determine real world distances frommeasurements performed over a 2D image generated from a real-world scene(e.g., the video received at step 207). Described below is the processfor applying perspective divide methods to estimate real world distanceof points located over the ground plane. FIG. 7 provides a diagramshowing the application 700 of perspective divide to one example case.For example, FIG. 7 shows the various elements involved in theperspective divide calculation. These elements may correspond to areal-life setting where a camera is mounted on a vehicle (e.g., facingforward on the dashboard).

At step 221, the dynamic driving metric output platform 102 maydetermine a height, the focal length and the tilt angle of the videocamera 103. In some instances, the dynamic driving metric outputplatform 102 may determine a height H that the video camera 103 islocated above the ground plane. In some instances, the height may bedetermined based on vehicle information (e.g., dimensions) received froma vehicle database (e.g., vehicle attribute database 104).

At step 222, the dynamic driving metric output platform 102 maydetermine a ground plane. In some instances, the dynamic driving metricoutput platform 102 may determine that the ground plane is the actualroad, which may be assumed to be flat over the duration of a drivingtrip.

At step 223, the dynamic driving metric output platform 102 maydetermine a horizon line, corresponding to a line that starts at thecenter of projection and continues towards what is called on perspectivethe vanishing point. For example, when an image of the road is observed,the right and the left lanes that are closest to the vehicle might notbe parallel in the 2D image even though they may be parallel in the realworld. If these two lanes are extrapolated in the image, they intersectat an imaginary point. This imaginary point may be referred to as avanishing point in projection. As illustrated in FIG. 6, the vanishingpoint may be located at infinity if one were to continue in thedirection facing forward over the horizon line. Accordingly, the dynamicdriving metric output platform 102 may determine a representation of thevanishing point on the 2D image (which may be obtained from the imageplane on the scene represented by FIG. 6) that may be located on theexact center of the image.

Referring to FIG. 2F, at step 224, the dynamic driving metric outputplatform 102 may determine a ground line (more specifically the dynamicdriving metric output platform 102 determines the 2D representation ofthe ground line). In some instances, the point of interest may belocated over a line that is at the intersection of two orthogonalplanes. These planes may be the ground plane (which is a horizontalplane) and a vertical plane that goes through the middle of the imageplane. In some instances, this vertical plane may bisect the image planeand thus it shall be referred to as the bisecting plane. Therefore, atthis step, the dynamic driving metric output platform 102 may befocusing on finding distances along the line at the intersection of thetwo orthogonal planes mentioned above, referred to herein as the “groundline”. This ground line is precisely the one over which the distance Dis marked on FIG. 6. FIG. 8 illustrates the bisecting plane 800 thatcontains all the geometry relationships of interest as were determinedabove at steps 221-224. For example, FIG. 8 illustrates a moresimplified view of the different elements involved on the perspectivedivide method described by steps 221-224. The 2D representation of theground line is determined according to the procedure mentioned belowusing the vanishing point.

As previously indicated, the objective of using this perspective dividemethod is to find the distance D between a point of interest located onthe ground plane and the center of projection. At step 225, the dynamicdriving metric output platform 102 may determine the distance D (whichwe will name longitudinal distance) using equation three:

$\begin{matrix}{D = \frac{{fl} \times H}{d}} & (3)\end{matrix}$

In equation three above, fl is the camera's focal length (which is thedistance between the center of projection and the image plane), and H isthe video camera's 103 height above the ground plane. This isillustrated in FIG. 7, which shows that the line connecting the centerof projection and the point of interest on the ground plane interceptsthe image plane at a point that is at a distance d below the middle ofthe image plane. In some instances, the distance d on equation three maybe measured in pixels and consequently the focal length may also beexpressed in units of pixels. In some instances, D may be expressed inthe same units chosen for H (for example feet, meters, or the like).

In some instances, the dynamic driving metric output platform 102 mayuse Equation three to determine distances in front of the video camera103 for points in the ground plane that are also contained in thebisecting plane. However, it may be difficult to obtain these distanceswithout knowing the focal length fl or the height H.

In some instances, the dynamic driving metric output platform 102 mayestimate distance without information on parameters of the video camera103 such as focal length and height above the ground plane. In theseinstances, the dynamic driving metric output platform 102 may useintermittent white road markings on U.S roads having a standard anduniversal length of ten feet to estimate distance. Furthermore, thestandard space between these white road markings is thirty feet. Thedynamic driving metric output platform 102 may apply the perspectivedivide equation (equation three) in a particular way to exploit thedistance information mentioned above. For example, the dynamic drivingmetric output platform 102 may apply the perspective divide equation asfollows (equations four, five, and six respectively):

$\begin{matrix}{{D\; 1} = \frac{{fl} \times H}{d\; 1}} & (4) \\{{D\; 2} = \frac{{fl} \times H}{d\; 2}} & (5) \\{{{D\; 1} - {D\; 2}} = {{fl} \times H \times \left( {\frac{1}{d\; 1} - \frac{1}{d\; 2}} \right)}} & (6)\end{matrix}$

In some instances, the dynamic driving metric output platform 102 mayapply equations four and five above to determine the real-worlddistances D1 and D2 for two points located on the ground plane. In theseinstances, H may represent the height of the camera above the groundplane and fl may represent the focal length of the video camera 103. Thedynamic driving metric output platform 102 may then use equation six todetermine the difference in real world distance between the two pointson the ground plane. In doing so, the dynamic driving metric outputplatform 102 may associate D1 and D2 to points in the ground plane forwhich D1-D2 is a known value. In these instances, the ground line may beparallel to the longest borders of the white road marking rectanglebecause most of the time the ego-vehicle may move forward in a directionparallel to the lines demarked by these white markings. Accordingly, thebeginning and the end of a white road marking may be ideal candidatesfor the two points mentioned above (for D1 and D2), however these pointsmay only work if the ground line would go through the centroid of thewhite road marking (or in other words if the white road marking would belocated at the center of the image plane). In a real-life situation thismight not be the case.

In some instances, the white road markings may be located towards theleft or towards the right of the middle of the image frame (because theego-vehicle may be driving on the middle of the lane demarked by theleft and by the right white road markings). In these instances, theperspective divide calculation mentioned previously may account for thisfact since the distance calculation considered points on the ground linewhich may be contained by the bisecting plane. In some instances, allthe real-world points belonging to the white road markings might not becontained in the bisecting plane. In these instances, the dynamicdriving metric output platform 102 may determine pan angle of the videocamera 103 is close to zero, and that, accordingly, horizontal lines in3D space may still be horizontal when these lines are represented in 2Dspace by the video camera 103 (e.g., in 3D world a pan angle close tozero may be equivalent to having the image plane orthogonal or veryclose to being orthogonal to the longest borders of the white roadmarkings located at the right and at the left of the ground line).Therefore, in the 2D image the dynamic driving metric output platform102 may project the beginning and the end of every white road markingover the 2D representation of the ground line by obtaining the interceptpoints over this line of the horizontal lines aligned with the beginningand with the end of the white road markings. In doing so, the dynamicdriving metric output platform 102 may obtain distances d1 and d2.

In the 3D world, when horizontal lines aligned with the beginning andwith the end of the white road marking are projected orthogonally overthe ground line, they may generate two intercept points (e.g., theprojections) on the ground line and the distance between these interceptpoints may be ten feet. This is because the ground line may be parallelto the longest border of the white road marking and the distance betweenthe beginning and the end of the white marking may translate to thedistance between intercept points in the ground line by applyingEuclidian geometry. Additionally, in the 3D world the distance from thevideo camera's 103 image plane to the horizontal lines aligned with thebeginning and with the end of the white road markings may correspond toD2 and D1 respectively. FIG. 9 shows a sketch 900 of the ground plane infront of the video camera 103 from the bird's eye point of view and itillustrates the descriptions mentioned above for the 3D scene. FIG. 10provides a sketch 1000 of the 2D representation generated by the videocamera 103 of the scene shown in FIG. 9.

FIG. 10 shows the projection over the 2D line representing the groundline of three horizontal lines. The first two horizontal lines maycorrespond to the left white road marking closer to the video camera 103and the dynamic driving metric output platform 102 may generate theintercept points associated with distances d1 and d2 using thesehorizontal lines. FIG. 10 also shows one more horizontal line thatcorresponds to the beginning of the next left white road marking. Insome instances, the dynamic driving metric output platform 102 may usethe intercept point corresponding to the next left white road marking todetermine the distance d3. FIG. 10 shows that there may be a dashed linethat runs through the middle of the white road markings over the leftthat is called the line that represents the left lane. Similarly, FIG.10 shows that there may be a dashed line that represents the right lane.

FIG. 10 also shows that the two lines representing the left and theright lane may intercept each other at a point called the vanishingpoint. In accordance with the concepts described above with regards toperspective transformations, the vanishing point may be the 2Drepresentation of the horizon-line mentioned above. In FIG. 9, thevanishing point may be the center of the image plane (in some instances,image plane and image frame may have the same center and these terms maybe used interchangeably). In these instances, the video camera 103'stilt and pan angles may be zero. In real-life camera mounting situationsthese angles may deviate slightly from zero. In these instances, thevanishing point might not be co-located with the center of the imageframe (or the center of the image plane) and thus the vanishing pointmay be above or below the center of the image frame depending on thevideo camera 103's tilt making a negative or a positive angle with thehorizon line. In instances of having a pan angle slightly different fromzero, the vanishing point may be slightly to the right or to the left ofthe center of the image frame. To account for small pan anglevariations, the dynamic driving metric output platform 102 may calculatethe vanishing point (obtained from the intersection of the 2D linesrepresenting the right and the left lane) and may use the vanishingpoint to determine the location of the 2D line representing the groundline (which may be a vertical line going through the vanishing point andmay be slightly to the right or to the left of the center of the imageframe). Accordingly, the dynamic driving metric output platform 102might not rely on the center of the image frame. In instances of anon-zero pan angle, the 2D line representing the vanishing point may beslightly to the left or to the right of the center of the image frame.

In the procedure illustrated by FIG. 10, d1 may be equal to the distancein pixels from the y-coordinate of the middle point of the 2D imageframe (which may be co-located with the vanishing point) to they-coordinate of the intercept point obtained by projecting a horizontalline from the beginning of the white road marking to the 2Drepresentation of the ground line. Similarly, the dynamic driving metricoutput platform 102 may obtain d2 by measuring the distance in pixelsbetween the y-coordinates of the middle of the image frame and theintercept point associated with the end of the white road marking.

From equation six above, the dynamic driving metric output platform 102may obtain the value of the product of focal length and height above theground plane. Equation seven shows this:

$\begin{matrix}{{{fl} \times H} = \frac{{D\; 1} - {D\; 2}}{\left( {\frac{1}{d\; 1} - \frac{1}{d\; 2}} \right)}} & (7)\end{matrix}$

All the values on the right side of equation seven may be known to thedynamic driving metric output platform 102. Furthermore, the dynamicdriving metric output platform 102 may determine that the value of D1-D2is ten feet. Therefore, the dynamic driving metric output platform 102may obtain any distance in front of the vehicle without knowing eitherthe focal length or the height above the ground plane by using theactual product of both values from equation seven above. For example,the dynamic driving metric output platform 102 may determine areal-world distance of a random point at a distance of d_pt pixels fromthe middle point of the image frame (measured over the 2D verticalline). Equation eight below provides the value of the distance D_pt infeet.

$\begin{matrix}{{D\_ pt} = \frac{{fl} \times H}{d\_ pt}} & (8) \\{{D\_ pt} = {\left( \frac{1}{d\_ pt} \right) \times \frac{\left( {{D1} - {D2}} \right)}{\left( {\frac{1}{d\; 1} - \frac{1}{d\; 2}} \right)}}} & (9)\end{matrix}$

The dynamic driving metric output platform 102 may determine equationnine by applying equation seven on equation eight. All the values on theright side of equation nine may be known to the dynamic driving metricoutput platform 102. Therefore, it might not necessary for the dynamicdriving metric output platform 102 to know the actual focal length valueor the actual value of the height of the video camera 103 above theground.

In some instances, the tilt angle of the video camera 103 might not bezero. In these instances, the dynamic driving metric output platform 102may perform a refined distance estimation method at step 225. FIG. 11illustrates a sketch 1100 when the video camera 103 has a tilt angle α.In this example, the image plane may be tilted by this same angle. FIG.11 shows the Horizon Line determined above at step 223. In this example,the Horizon Line may be represented by a single point on the image planeand located at a distance of p pixels from above the center of the imageplane as FIG. 11 shows.

FIG. 11 shows that the point of interest in the ground plane isrepresented on the image plane as a point located at a distance of dpixels from the center of the image plane. In this instance, the dynamicdriving metric output platform may modify the perspective dividecalculations change as follows (equations ten to twelve respectively):

$\begin{matrix}{{{Tan}\left( {\beta - \alpha} \right)} = \frac{d}{fl}} & (10) \\{{{Tan}(\beta)} = \frac{H}{D}} & (11) \\{D = \frac{H \times \left( {1 - {{\tan(\alpha)} \times \left( \frac{d}{fl} \right)}} \right)}{\frac{\alpha}{fl} + {{Tan}(\alpha)}}} & (12)\end{matrix}$

Equations ten through twelve summarize the relationships used by thedynamic driving metric output platform 102 to solve for the distance,which is the objective of the calculation. As shown in FIG. 11, there isan additional useful relationship, which provides an expression for theangle α as follows (equation thirteen):

$\begin{matrix}{{{Tan}(\alpha)} = \frac{p}{fl}} & (13)\end{matrix}$

In applying equation thirteen to equation twelve, the dynamic drivingmetric output platform 102 may determine equation fourteen:

$\begin{matrix}{D = \frac{H \times \left( {{fl}^{2} - {p \times d}} \right)}{{fl} \times \left( {d + p} \right)}} & (14)\end{matrix}$

FIG. 12 shows a sketch 1200 of the same type of 2D scene shown in FIG.10 for points aligned with the beginning and with the end of white roadmarkings. However, in this instance, the video camera's 103 tilt anglemay be non-zero. As FIG. 12 shows, the center of the image frame mightnot be collocated with the vanishing point.

Using the representation illustrated by FIG. 12 and applying the samecriterion used before for the difference of distance between the pointsin the ground line aligned to the beginning and to the end of the whiteroad marking, the dynamic driving metric output platform 102 may obtainthe following relationship (equation fifteen):

$\begin{matrix}{{{D1} - {D\; 2}} = {\frac{H}{fl} \times \left( {\frac{\left( {{fl}^{2} - {p \times d\; 1}} \right)}{\left( {{d1} + p} \right)} - \frac{\left( {{fl}^{2} - {p \times d\; 2}} \right)}{\left( {{d2} + p} \right)}} \right)}} & (15)\end{matrix}$

As shown in equation fifteen, H and fl might not be a product anymore.Thus, the dynamic driving metric output platform 102 might not be ableto obtain the value of H multiplied by fl and apply it to all thedistance calculations as in equation nine. Therefore, in this instance,the dynamic driving metric output platform 102 may determine the valuesof H and fl individually. Accordingly, equation fifteen may be betterexpressed in terms of the angle α than the focal length. After applyingequation thirteen to equation fifteen, the dynamic driving metric outputplatform 102 may obtain equation sixteen below in terms of H and α.

$\begin{matrix}{{{D1} - {D\; 2}} = {\frac{H}{{Tan}(\alpha)} \times \left( {\frac{\left( {p - {\left( {d\; 1} \right) \times \left( {{Tan}(\alpha)} \right)^{2}}} \right)}{\left( {{d1} + p} \right)} - \frac{\left( {p - {\left( {d\; 2} \right) \times \left( {{Tan}(\alpha)} \right)^{2}}} \right)}{\left( {{d\; 2} + p} \right)}} \right)}} & (16)\end{matrix}$

Before getting into how equation sixteen may be used to obtain thevalues of H and α, two other important aspects of the method should beaddressed which are the vanishing point and the value of which may bethe distance between the point representing the horizon line and themiddle of the image plane. In practice, the value of p may be calculatedby the dynamic driving metric output platform 102 by obtaining thevanishing point on the image frame for all the lines that according toperspective projection should converge on the horizon. The dynamicdriving metric output platform 102 may determine the vanishing point byusing the same white road markings found through lane detection methods.In this instance, the dynamic driving metric output platform 102 may useany image frame obtained from the driving video received at step 207.There may be two white road markings on both sides and it may bestraightforward for the dynamic driving metric output platform 102 todetect both of them through lane detection methods. The dynamic drivingmetric output platform 102 may then find the intersection of the linesrepresenting the right and the left lane. The point where both projectedlines intersect may be the vanishing point and thus the dynamic drivingmetric output platform 102 may have found the point in the image framerepresenting the horizon line. Even if only one set of white roadmarkings is available, the dynamic driving metric output platform 102may find sets of lines that are parallel on the ground plane and thatintersect on the 2D image. For instance, the road may have a visibleborder that may be detected through computer vision methods. The dynamicdriving metric output platform 102 may use this border with one set ofwhite road markings (e.g., left or right markings) to find theintersection of the road border with the line representing the lane(e.g., for the visible white road markings). Accordingly, in someinstances, the dynamic driving metric output platform 102 might not usethe other set of white road markings to determine the vanishing point.There may be multiple ways for the dynamic driving metric outputplatform 102 to determine the vanishing point from the image frame.

In some instances, the dynamic driving metric output platform 102 mightdetermine the distance p by calculating the difference between they-coordinate of the vanishing point mentioned above and the y-coordinateof the middle of the image frame. Based on FIG. 12, the dynamic drivingmetric output platform 102 is able to measure d1 and d2 and may store avalue for D1-D2 of ten feet. Therefore, at this point except for H and αall the other values on equation sixteen may have been previouslydetermined by the dynamic driving metric output platform 102. To findthe values of H and α, the dynamic driving metric output platform 102may perform an exhaustive search (e.g., a numerical optimization method)in order to find the possible combination of these values that mayminimize the error obtained by calculating the absolute differencebetween D1-D2 and the value of ten (given that D1-D2 should be tenfeet). Therefore, for the implementation of the exhaustive search thedynamic driving metric output platform 102 may select a combination of Hand α values and may determine the actual value of the differencebetween D1-D2 and ten for this combination. Here, the dynamic drivingmetric output platform 102 may determine D1-D2 by invoking equationsixteen above. The corresponding error generated by this combination isobtained by the dynamic driving metric output platform 102 as follows(equation seventeen):Error=abs(D1−D2−10)  (17)

In applying equation seventeen, the dynamic driving metric outputplatform 102 may use the absolute value of the difference to generatethe error. After all the combinations of H and α values are processed bythe dynamic driving metric output platform 102, the dynamic drivingmetric output platform 102 may generate a list of errors correspondingeach to a different combination. The dynamic driving metric outputplatform 102 may then select a combination as the solution of theexhaustive search by determining which one generated the minimum erroracross the list.

The dynamic driving metric output platform 102 may set the range for thesearch of H between two and four, which represents two feet and fourfeet respectively (e.g., this represents the range of heights for cameramounting on the dash board or the windshield of a vehicle). The dynamicdriving metric output platform 102 may set the range for the search of abetween zero and twenty degrees (the actual tilt angle is expected to bemuch lower than twenty degrees). The dynamic driving metric outputplatform 102 may set the step size for the search at 0.1 for H and 0.1for α.

In some instances, the dynamic driving metric output platform 102 mayinclude two consecutive white road markings when performing theexhaustive search. In this instance, the dynamic driving metric outputplatform 102 may improve the accuracy of the exhaustive search byincorporating as input the distance corresponding to the beginning ofthe next white road marking, which is designated on FIG. 9 as D3. Inthis instance, this white road marking is thirty feet apart from the endof the current white road marking. In this instance, the 2D distancecorresponding to D3 according to the perspective divide calculation isdesignated as d3, which is illustrated in FIG. 12. To account for D3 andd3, the dynamic driving metric output platform 102 may modify equationsixteen as follows (equation eighteen):

$\begin{matrix}{{{D3} - {D\; 1}} = {\frac{H}{{Tan}(\alpha)} \times \left( {\frac{\left( {p - {\left( {d\; 3} \right) \times \left( {{Tan}(\alpha)} \right)^{2}}} \right)}{\left( {{d\; 3} + p} \right)} - \frac{\left( {p - {\left( {d\; 1} \right) \times \left( {{Tan}(\alpha)} \right)^{2}}} \right)}{\left( {{d\; 1} + p} \right)}} \right)}} & (18)\end{matrix}$

In some instances, the dynamic driving metric output platform 102 mayincorporate equation eighteen into the exhaustive search computation,and may change the error function as follows (equation nineteen):Error=abs(D1−D2−10)+abs(D3−D1−30)  (19)

Thus, the dynamic driving metric output platform 102 may apply equationsixteen above to the exhaustive search and this error function (orobjective function) may weight equally the two individual contributorsto the error (the two contributors are the absolute differences). Theranges for the search may be the same as the ones mentioned above. Theonly thing that changes in this instance may be the error function. Ateach point in the search, the dynamic driving metric output platform 102may compute D1-D2 using equation sixteen and may compute D3-D1 fromequation eighteen.

After application of the exhaustive search, the dynamic driving metricoutput platform 102 may obtain the combination of H and α values thatminimize the error expressed by equation nineteen. After obtaining α,the dynamic driving metric output platform 102 may use equation thirteento obtain the focal length fl. This completes the method and the dynamicdriving metric output platform 102 may estimate the focal length fl,height H, and tilt angle α. Once the dynamic driving metric outputplatform 102 obtains these parameters, the dynamic driving metric outputplatform 102 may apply equation fourteen to obtain the distance to anypoint over the ground line in front of the vehicle.

The method described above may rely on one set of white road markings(the left white road markings for the cases illustrated on FIGS. 8-12).It may be possible to include the white road markings over the right toincrease accuracy. Similarly, the method to estimate H and α describedabove may use only one image frame from the driving video. The methodmay be extended to use multiple image frames in which case the dynamicdriving metric output platform may apply regression methods in order tofind the H and α values that would minimize the overall error over allthe image frames used. This may increase the robustness of theestimation method.

At step 226, the dynamic driving metric output platform 102 may generatedriving metric output information and one or more commands directing theaccident analysis platform 105 to cause display of a driving metricoutput interface based on the driving metric output information. In someinstances, the driving metric output information may correspond to thevertical distance determined at step 225. Additionally or alternatively,the driving metric output information may include the speed determinedat step 215. It should be understood that the calculation of a distancemay be performed in addition to or in lieu of the speed determinationpreviously discussed.

At step 227, the dynamic driving metric output platform 102 may send thedriving metric output information and the one or more commands directingthe accident analysis platform 105 to cause display of the drivingmetric output interface based on the driving metric output information.In some instances, the dynamic driving metric output platform 102 maysend the driving metric output information and the one or more commandsdirecting the accident analysis platform 105 to cause display of thedriving metric output interface based on the driving metric outputinformation via the communication interface 113 and while the secondwireless data connection is established.

At step 228, the accident analysis platform 105 may receive the drivingmetric output information and the one or more commands directing theaccident analysis platform 105 to cause display of the driving metricoutput interface based on the driving metric output information. In someinstances, the accident analysis platform 105 may receive the drivingmetric output information and the one or more commands directing theaccident analysis platform 105 to cause display of the driving metricoutput interface based on the driving metric output information whilethe second wireless data connection is established.

Referring to FIG. 2G, at step 229, the accident analysis platform 105may generate the driving metric output interface and may cause displayof the driving metric output interface via a display of the accidentanalysis platform 105. In causing display of the driving metric outputinterface, the accident analysis platform 105 may generate an interfacesimilar to graphical user interface 1305, which is shown in FIG. 13. Forexample, graphical user interface 1305 may indicate a distance betweenthe ego vehicle and one or more other vehicles, obstructions, objects,infrastructure, or the like. In some instances, graphical user interface1305 may be a graphical overlay on additional content (e.g., the videofootage).

At step 230, the dynamic driving metric output platform 102 maydetermine one or more horizontal distances. For example, step 229describes calculation of distances of points in the ground line thatbelongs to the bisecting plane that goes through the middle of the imageplane. For all the other points on the ground plane, the dynamic drivingmetric output platform 102 may extend the distance calculation byapplying the perspective divide method described herein. Extension ofthis distance calculation is described with regards to step 230. Itshould be understood that horizontal distances may be determined by thedynamic driving metric output platform 102 in addition to or in lieu ofthe calculated speeds and vertical distances described above. In someinstances, there may be a camera tilt angle equal to zero. In otherinstances, there may be a camera tilt angle not equal to zero. In theseinstances, Euclidian geometry may be applied to extend the methodsdescribed herein.

From the exhaustive search methods described previously, the dynamicdriving metric output platform 102 may obtain the focal length and theHeight of the video camera 103. Up to this point, the dynamic drivingmetric output platform 102 performed all the perspective dividecalculations over a vertical plane named the bisecting plane. Now armedwith the focal length information (obtained from the exhaustive searchmethod described previously), the dynamic driving metric output platform102 may effectively perform these calculations horizontally instead ofvertically. Therefore, the dynamic driving metric output platform 102may calculate the real-world distance of any point in the ground plane.FIG. 13 illustrates the application of this method.

FIG. 14 shows a sketch 1400 in which the point of interest is in theground plane however this time it might not be located over the groundline. To calculate the distance from the video camera 103 to the pointof interest, the dynamic driving metric output platform 102 maycalculate the values of D and L. To obtain the value of D, the dynamicdriving metric output platform 102 may perform an orthogonal projectionfrom the point of interest to the ground line. The projected pointallows the dynamic driving metric output platform 102 to define thedistance D as FIG. 12 shows. From the methods outlined above, thedynamic driving metric output platform 102 may determine the values ofD, H and fl. Therefore, the dynamic driving metric output platform 102may calculate L. FIG. 14 shows that the line that connects the point ofinterest with the center of projection intercepts the image plane at apoint that is located w pixels away from the center of the image plane.Applying perspective divide calculations to the triangles that includethe sides with length w and length L, the dynamic driving metric outputplatform 102 determines the following relationship (equation twenty):

$\begin{matrix}{L = \frac{w \times D}{fl}} & (20)\end{matrix}$

As described above, the distances measured over the ground-plane wereconsidered from the point in the ground plane that corresponded to theposition of the video camera 103 (the center of projection has its ownprojection on the ground plane and the dynamic driving metric outputplatform 102 used this projection to establish distances over the groundline previously). With the knowledge of L and D, the dynamic drivingmetric output platform 102 may determine the distance over the groundplane of the video camera 103 to the point of interest which becomes√D²+L² (more precisely it may be the distance between the orthogonalprojection of the center of projection over the ground plane to thepoint of interest which may also be over the ground plane). In someinstances, the dynamic driving metric output platform 102 may attempt todetermine a distance between the center of projection and the point ofinterest. In these instances, the point of interest value may be√D²+L²+H².

The development illustrated in FIG. 14 may be extended to incorporatepoints of interest that might not be located in the ground plane. FIG.15 shows a sketch 1500 of a point of interest that is at a distance Z(e.g., in feet) above the ground plane. FIG. 14 shows the general caseof any point of interest that may be located anywhere in the 3D space infront of the video camera 103. As long as the dynamic driving metricoutput platform 102 can find the orthogonal projection of such point ofinterest above over the ground plane, the dynamic driving metric outputplatform 102 may find the distance of the video camera's 103 center ofprojection to such point of interest located anywhere on 3D space. The2D representation of such orthogonal projection over the ground planecan be obtained through computer vision methods (for instance if thepoint of interest is the centroid of a traffic sign in the road, theorthogonal projection of such centroid over the ground plane will be thepoint where the traffic sign's pole is attached to the road and thisattachment point can be determined through computer vision objectdetection and image segmentation methods).

The procedure to obtain the distance from the video camera 103 to thepoint of interest is to first consider the point that results from theprojection of the point of interest over the ground plane (point A). Thedynamic driving metric output platform 102 may project point A over theground line and the point resulting from this projection may define thedistance D in the same way shown in FIG. 14. The methods outlined abovemay be used to determine D, H and the focal length fl. The procedureexplained above in connection with FIG. 14 and equation twenty can beused by the dynamic driving metric output platform 102 to obtain L. Theremaining part is to calculate the distance Z above the ground plane.

To determine Z the dynamic driving metric output platform 102 may firstproject the point of interest over the bisecting plane as FIG. 15 shows(point B). For a distance Z that is lower than H the projection point Bmay be at a distance H−Z below the horizon line. The line that connectsthe center of projection to point B may intercept the image plane at adistance r below the horizon line. The dynamic driving metric outputplatform 102 may use triangles defined over the bisecting plane thatinclude the sides with length r and length H−Z to define the followingrelationship (equation twenty one):

$\begin{matrix}{{H - Z} = \frac{r \times D}{fl}} & (21)\end{matrix}$

Therefore, since the dynamic driving metric output platform 102 knows H,it may use equation twenty one to determine Z. Based on D, L and Z, thedynamic driving metric output platform 102 may obtain the distancebetween the center of projection and the point of interest as:√D²+L²+(H−Z)². This completes the procedure and based on all therelationships derived the dynamic driving metric output platform mayhave the ability to determine distances to any point in 3D space infront of the ego-vehicle.

At step 231, the dynamic driving metric output platform 102 may generatedriving metric output information and one or more commands directing theaccident analysis platform 105 to cause display of a driving metricoutput interface based on the driving metric output information. In someinstances, the driving metric output information may be the distance tothe point of interest determined at step 230. In some instances, thisdriving metric output information may be determined in addition to or inlieu of previously determined driving metric output information.

At step 232, the dynamic driving metric output platform 102 may send thedriving metric output information and the one or more commands directingthe accident analysis platform 105 to cause display of the drivingmetric output interface based on the driving metric output information.In some instances, the dynamic driving metric output platform 102 maysend the driving metric output information and the one or more commandsdirecting the accident analysis platform 105 to cause display of thedriving metric output interface based on the driving metric outputinformation via the communication interface 113 and while the secondwireless data connection is established.

At step 233, the accident analysis platform 105 may receive the drivingmetric output information and the one or more commands directing theaccident analysis platform 105 to cause display of the driving metricoutput interface based on the driving metric output information. In someinstance, the accident analysis platform 105 may receive the drivingmetric output information and the one or more commands directing theaccident analysis platform 105 to cause display of the driving metricoutput interface based on the driving metric output information whilethe second wireless data connection is established.

Referring to FIG. 2H, at step 234, the accident analysis platform 105may generate and display a driving metric output interface. In someinstances, in displaying the driving metric output interface, theaccident analysis platform 105 may cause display of a graphical userinterface similar to graphical user interface 1605, as shown in FIG. 16.For example, the accident analysis platform 105 may display a distanceto the point of interest. In some instances, the graphical userinterface 1605 may be an overlay over existing content (e.g., the videofootage). In some instances, the graphical user interface 1605 mayinclude additional driving metrics, such as a previously calculatedspeed or distance.

The methods outlined above for distance estimation of points of interestover the ground plane may be used to determine distances between thevideo camera's 103 center of projection and any object that has physicalcontact with the ground plane. Examples of such objects includestelephone poles, light poles, traffic light poles, traffic sign poles,pedestrians and of course other vehicles. In case of other vehicles, thedistance estimation may provide the distance between the plane of thevideo camera 103 and the plane that represents the back of the vehiclein front of the video camera 103. The 2D representation of the back ofthe vehicle may be the actual bounding box that is provided by severalof the deep learning based object detection methods available. Thus,distance determination for vehicles may be provided as illustrated bysketch 1700 in FIG. 17.

FIG. 17 is similar to FIG. 7 with the addition of a plane representingthe back of a vehicle and also a plane that represents the plane of thevideo camera 103 (this plane contains the center of projection). Asshown in FIG. 17, the plane of the video camera 103 may be parallel tothe image plane and parallel to the plane representing the back of thevehicle (the three planes mentioned may be orthogonal to the groundplane). Therefore, the method to determine distances to points ofinterest in the ground plane allows the dynamic driving metric outputplatform 102 to simultaneously determine distances between the plane ofthe video camera 103 and the plane representing the back of the vehiclein front of the video camera 103.

In some instances, the vehicle in front of the video camera 103 mightnot be at a location such that the ground line intercepts the planerepresenting the back of this vehicle. In other words, the vehicle maybe in a lane that is adjacent to the one where the ego-vehicle isdriving (lane in this context means the traffic lane where one singlevehicle fits). In this instance, the dynamic driving metric outputplatform 102 may still determine the distances between the plane of thevideo camera 103 and the plane of the back of the vehicle in theadjacent lane using the same methods. In this last instance, the dynamicdriving metric output platform 102 may extend horizontally the planerepresenting the back of the vehicle in the adjacent lane until thisplane is intercepted by the ground line. Then the point of interceptionmay become the point of interest and the dynamic driving metric outputplatform 102 may calculate the distance to such point. Thus, the dynamicdriving metric output platform 102 may obtain the distance from theplane of the video camera 103 to the plane representing the back of thevehicle in the adjacent lane.

In one or more instances, the dynamic driving metric output platform 102may determine the lateral distance to such vehicle in the adjacent lane,since the method described in the paragraph above provides longitudinaldistance (which is the distance in the direction of travel). In one ormore instances, this may depend on how to account for the position ofthe vehicle in the adjacent lane. If lateral distance is considered asthe distance to the side of the vehicle then the dynamic driving metricoutput platform 102 may determine from the computer vision objectdetection method (or deep learning based method) any point on the sideof the vehicle and then project such point over the ground plane. Theprojected point may become the point of interest and the dynamic drivingmetric output platform 102 may calculate the distance L using theprocedure illustrated by FIG. 14 and described by equation twenty.Distance L may then become the lateral distance.

Similar procedures to the one described above for distance determinationof surrounding vehicles may be used to determine distances topedestrians, bicyclists, traffic light poles, etc. Additionally, giventhe fact that the dynamic driving metric output platform 102 maydetermine relative distances between the ego-vehicle and surroundingvehicles we may have an alternate method to determine relative speed (inaddition to the method described in steps 208-215) by dividing thedifference in distances from one frame to the next by the time inseconds that elapses between frames. This method is further describedbelow. Similarly, relative acceleration may be determined by dividingthe relative speeds calculated between two frames by the time in secondsthat elapses between frames. Lateral relative speed may be similarlycalculated using relative lateral distance and lateral acceleration maybe obtained from lateral relative speed. Absolute speed (longitudinaland lateral) may be obtained for all vehicles by adding vectorially theabsolute speed of the ego-vehicle to the relative speed of thesurrounding vehicle. Absolute acceleration of the surrounding vehiclemay be similarly obtained by adding vectorially the absoluteacceleration of the ego-vehicle to the relative acceleration of thesurrounding vehicle.

In some embodiments, distance may be measured from the plane of thevideo camera 103. Additionally or alternatively, the distance from thefront of the ego-vehicle to the back of the surrounding vehicle may beobtained by subtracting the length of the front part of the car from thedistance as originally calculated. The length of the front part of thecar may be estimated in average to be five feet (this is effectively thedistance between the windshield and the front of the vehicle).Additionally, if the vehicle model is known then the particular lengthmay be introduced in the calculations. In some instances, the dynamicdriving metric output platform 102 may generate one or more commandsdirecting a vehicle attribute database 104 to provide the particularlength based on the vehicle model. For instance, at step 235, thedynamic driving metric output platform 102 may receive generate one ormore commands directing the vehicle attribute database 104 to determinethe vehicle parameters. In some instances, the calculations of speed andaccelerations might not be influenced by this modification to thecalculation of distance.

At step 236, the dynamic driving metric output platform 102 mayestablish a connection with the vehicle attribute database 104. Forexample, the dynamic driving metric output platform 102 may establish athird wireless data connection with the vehicle attribute database 104to link the vehicle attribute database 104 to the dynamic driving metricoutput platform 102.

At step 237, the dynamic driving metric output platform 102 may send theone or more commands directing the vehicle attribute database 104 toprovide the particular length based on the vehicle model. In someinstances, the dynamic driving metric output platform 102 may send theone or more commands directing the vehicle attribute database 104 toprovide the particular length based on the vehicle model via thecommunication interface 113 and while the third wireless data connectionis established.

At step 238, the vehicle attribute database 104 may receive the one ormore commands directing the vehicle attribute database 104 to providethe particular length based on the vehicle model. In some instances, thevehicle attribute database 104 may receive the one or more commandsdirecting the vehicle attribute database 104 to provide the particularlength based on the vehicle model while the third wireless dataconnection is established.

With reference to FIG. 2I, at step 239, the vehicle attribute database104 may determine particular length. For example, the vehicle attributedatabase 104 may maintain an index based on vehicle make, model,identification number, or the like, and may index the vehicle todetermine the length corresponding to a distance between the windshieldand the front of the vehicle.

At step 240, the vehicle attribute database 104 may then send, to thedynamic driving metric output platform 102, a vehicle parameter outputcorresponding to the particular length. In some instances, the vehicleattribute database 104 may send the vehicle parameter output while thethird wireless data connection is established.

At step 241, the dynamic driving metric output platform 102 may receivethe vehicle parameter output from the vehicle attribute database 104. Insome instances, the dynamic driving metric output platform 102 mayreceive the vehicle parameter output from the vehicle attribute database104 via the communication interface 113 and while the third wirelessdata connection is established.

At step 242, the dynamic driving metric output platform 102 may updatethe driving metric output. In some instances, the dynamic driving metricoutput platform 102 may update the driving metric output by subtractingthe particular length from the distance determined. In addition, thedynamic driving metric output platform 102 may generate one or morecommands directing the accident analysis platform 105 to update thedriving metric interface based on the updated distance.

At step 243, the dynamic driving metric output platform 102 may send theupdated driving metric output and the one or more commands directing theaccident analysis platform 105 to update the driving metric interfacebased on the updated distance. In some instances, the dynamic drivingmetric output platform 102 may send the updated driving metric outputand the one or more commands directing the accident analysis platform105 to update the driving metric interface based on the updated distancewhile the second wireless data connection is established and via thecommunication interface 113.

At step 244, the accident analysis platform 105 may receive the updateddriving metric output and the one or more commands directing theaccident analysis platform 105 to update the driving metric interfacebased on the updated distance. In some instances, the accident analysisplatform 105 may receive the updated driving metric output and the oneor more commands directing the accident analysis platform 105 to updatethe driving metric interface based on the updated distance while thesecond wireless data connection is established.

Referring to FIG. 2J, at step 245, based on the received commands andthe updated driving metric output, the accident analysis platform 105may generate and display an updated driving metric interface. Indisplaying the updated driving metric interface, the accident analysisplatform 105 may cause display of a graphical user interface similar tographical user interfaces 1305 and 1605, which are described above. Insome instances, the graphical user interface may be generated inaddition to or in lieu of previously described graphical userinterfaces.

At step 246, the dynamic driving metric output platform 102 maydetermine an updated vehicle speed. After determining relative distancesto objects in front of the ego-vehicle, the dynamic driving metricoutput platform 102 may determine vehicle speed in an alternative methodto the absolute speed determination method described above at steps208-215. Previously the absolute speed determination was based on a 2Dmarker that the dynamic driving metric output platform 102 used toestablish time stamps from events when the marker touched the beginningand the end of white road markings. Now the dynamic driving metricoutput platform 102 has the ability use, for example, the beginning ofany white road marking and determine the longitudinal distance (e.g.,the distance of the plane of the video camera 103 to the projection ofthe beginning of the white road marking over the ground line) to suchbeginning. The dynamic driving metric output platform 102 may obtain thedifference of relative longitudinal distances to the beginning of thesame white road marking between two frames and then may estimateabsolute speed by dividing this difference by the time in seconds thatelapses between two consecutive image frames. In other words, thedynamic driving metric output platform 102 may determine now absoluteego-vehicle speed with just two consecutive image frames and might notwait for any 2D marker to touch the other end of the white road marking.This may provide greater resolution and flexibility to the absolutespeed estimation. Instead of using the beginning of the white roadmarking, the dynamic driving metric output platform 102 may similarlyuse the end of the white road marking or any reference point for thatmatter. Additionally, even if white road markings are not available, thedynamic driving metric output platform 102 may use any reference pointon other pavement markings such as the word “STOP” written on thepavement, or the word “ONLY” written on the pavement, or the arrowssigns in the pavement (FIGS. 3A-3B show the dimensions of some of theseother pavement markings) and as mentioned above the predefineddimensions of these other pavement markings can be used to extract thecamera parameters such as focal length, camera height and tilt angle.Furthermore, any object in the scene that has physical contact with theground plane may be used to determine the difference of relativedistances to such object from one image frame to the next. For instance,the dynamic driving metric output platform 102 may can determinelongitudinal distances to public light poles, to traffic light poles, totelephone poles and may use the difference in distances between imageframes to determine absolute speed in a similar way to that describedabove with regards to the white road markings. The detection of allthese poles may be provided by computer vision object detection methods(including deep learning based methods). Any static object that may bedetected on the street/road may be used to determine absolute speed. Asdescribed above, the updated speed may be sent to the accident analysisplatform 105 for display.

It should be understood that the presence of the white road markingsallows the dynamic driving metric output platform 102 not only toextract the camera parameters, but also to provide valuable referencesfor the real-life distances that correspond to the scene in front of thevehicle. The white road markings that come at far away distances may beused as references to provide a correction factor to the distanceestimation method presented in this invention. Since it is known thatthe distance between white road markings is always thirty feet, thisinformation may be used to provide correction for perspectivedistortion.

Subsequently, the example event sequence may end, and dynamic drivingmetric output platform may continue to generate dynamic driving metricoutputs in a similar manner as discussed above (e.g., by receiving videofootage from a vehicle mounted camera, extrapolating information fromthe video footage, calculating driving metrics, and directing theaccident analysis platform 105 to generate various interfaces to displaythe metrics). By operating in this way, dynamic driving metric outputplatform 102 may improve the quality of driving metrics to increasedriver safety as well as the quality of metrics available for driveranalysis.

FIG. 18 depicts an illustrative method for deploying a dynamic drivingmetric output platform that utilizes improved computer vision methods todetermine driving metrics in accordance with one or more exampleembodiments. Referring to FIG. 18, at step 1803, the dynamic drivingmetric output platform may establish a connection with a video camera.At step 1806, the dynamic driving metric output platform may generateone or more commands directing the video camera to capture videofootage. At step 1809, the dynamic driving metric output platform maysend the one or more commands directing the video camera to capture thevideo footage. At step 1812, the dynamic driving metric output platformmay receive the video footage. At step 1815, the dynamic driving metricoutput platform may determine whether a speed output should bedetermined. If a speed output should not be determined, the dynamicdriving metric output platform may proceed to step 1845. If a speedoutput should be determined, the dynamic driving metric output platformmay proceed to step 1818.

At step 1818, the dynamic driving metric output platform may determinewhether a distance was previously determined. If a distance waspreviously determined, the dynamic driving metric output platform mayproceed to step 1842. If a distance was not previously determined, thedynamic driving metric output platform may proceed to step 1821.

At step 1821, the dynamic driving metric output platform may detect roadmarkings in the video footage. At step 1824, the dynamic driving metricoutput platform may insert a reference marker into the video footage. Atstep 1827, the dynamic driving metric output platform may convert thevideo footage images to greyscale. At step 1830, the dynamic drivingmetric output platform may detect a brightness transition in the videofootage images. At step 1833, the dynamic driving metric output platformmay set a time stamp in response to detecting the brightness transition.At step 1836, the dynamic driving metric output platform may determinethe end of the marking and set an additional time stamp. At step 1839,the dynamic driving metric output platform may determine an amount oftime between the time stamps. At step 1842, the dynamic driving metricoutput platform may determine the vehicle speed. At step 1845, thedynamic driving metric output platform may determine whether a distanceshould be determined. If the dynamic driving metric output platformdetermines that a distance should not be determined, the dynamic drivingmetric output platform may proceed to step 1878. If the dynamic drivingmetric output platform determines that a distance should be determined,the dynamic driving metric output platform may proceed to step 1848.

At step 1848, the dynamic driving metric output platform may determine avideo camera height, a focal length, and a tilt angle. At step 1851, thedynamic driving metric output platform may determine a ground plane inthe video footage. At step 1854, the dynamic driving metric outputplatform may determine a horizon line in the video footage. At step1857, the dynamic driving metric output platform may determine a groundline in the video footage. At step 1860, the dynamic driving metricoutput platform may determine a distance (e.g., vertical distance,horizontal distance, or a combination of the two). At step 1863, thedynamic driving metric output platform may generate one or more commandsdirecting a vehicle attribute database to provide vehicle parameters. Atstep 1866, the dynamic driving metric output platform may establish aconnection with the vehicle attribute database. At step 1869, thedynamic driving metric output platform may send the one or more commandsdirecting the vehicle attribute database to provide vehicle parameters.At step 1872, the dynamic driving metric output platform may receive thevehicle parameter output. At step 1875, the dynamic driving metricoutput platform may update the distance determined based on the vehicleparameter output. At step 1878, the dynamic driving metric outputplatform may generate a driving metric output corresponding to the speedand/or distance and one or more commands directing an accident analysisplatform to display the driving metric output. At step 1881, the dynamicdriving metric output platform may establish a connection with theaccident analysis platform and may send the driving metric output andthe one or more commands directing the accident analysis platform todisplay the driving metric output.

It should be understood that all the methods presented herein have beendescribed based on a camera mounted in the ego-vehicle and facingforward. The methods and the principles described herein may be extendedto work with a camera facing backwards. Currently there are severalparking assistance cameras mounted on the back of the cars running onU.S. roads (several car manufacturers install back cameras). The feedfrom these back cameras may be exploited by a system that implements themethods described herein and all the algorithms that use speed,distance, and acceleration estimation (for ego and surrounding vehicles)may thus be extended to include the data extracted from the scene behindthe vehicle which may increase the coverage and predictive power of anyalgorithm/model that works with such data.

As previously mentioned, there are two primary arrangements describedherein. One is to allow the extraction of speed, distance, andacceleration information from existing datasets of driving video so thatresearchers may use the data to train and validate their models. In thissense, one additional benefit is the ability to use videos available onthe Internet showing dashboard camera footage of actual crashes. In thiscase, the researcher has priceless information about the differentrelative and absolute speeds, relative and absolute accelerations andall the relevant distances to all the surrounding vehicles for theseconds leading to the crash, besides having distances to relevantobjects in the scene (distances to pedestrians for example). Theinformation that may be extracted from footage of crashes is extremelyvaluable since these crashes might not be reproduced in laboratories oron other settings. Even if the crash is somehow staged (in a real-lifesetting, or through some software/simulation process that generates acrashing scenario) it may lack the human driver component and thereforethe staging will lack the valuable component of the human reactioninformation on the seconds leading to the crash. The footage of crasheshas involved the real-world risk and response of the driver of theego-vehicle and therefor the ability to use this information forresearch is priceless and it is irreproducible. Accordingly, obtainingsuch information may improve overall vehicle and driving safety. As itstands today, this source of information has not been tapped by theresearch community because of the inability to extract the distance,speed and acceleration metrics.

The second major application is the ability to provide a real-timesystem on-board the ego-vehicle that allows the extraction of distance,speed, and acceleration information from just the camera, withoutrequiring the user to set the camera at a specific height or to obtainfocal length information from the camera. In several cameras, the focallength changes also with the resolution chosen for the video (the usercould choose from a number of resolutions) so it may be an additionalcomplication for the user to obtain the corresponding focal lengthinformation for the resolution setting he chooses. Further, a real-timesystem that implements the methods presented herein may detect throughany computer vision based object detection method (including any deeplearning based method) any of the pavement markings described above inorder to extract the camera parameters. This may occur a few minutesafter the system is turned-on for the first time. Once the systemextracts the camera parameters from the pavement markings the system canstart providing distance, speed and acceleration informationimmediately. Additional pavement markings found on the road may be usedto further validate the extracted camera parameters such as height,focal length and camera tilt angle. In case of the pan angle, this maybe used as an additional parameter of the exhaustive search methodmentioned above and then the pan angle may be obtained. In this case,another vanishing point may be obtained by processing the intersectionof the lines that are horizontal or near horizontal in the image frame.As previously mentioned, before the pan angle is reasonably assumed tobe such that the plane of the camera and the image plane are parallel tothe front of the ego-vehicle, and parallel at the same time to the backof the surrounding vehicles that move in the same direction as theego-vehicle (this is considered a situation with a zero degrees panangle).

Herein, the camera's tilt and pan angle are discussed without mention ofthe roll angle. The roll angle may be such that the image frame has zerorotation in which case the roofs of the vehicles in front horizontal orvery close to being horizontal may be observed. If there is a roll anglethat generates a non-zero rotation it may be corrected through imageprocessing by detecting the angle of rotation (by measuring thedeviation angle of the lines that are supposed to be horizontal on thescene) and applying a counter rotation with the same angle value to thewhole image. In the typical case, the roll angle is such that there maybe close to zero rotation.

One or more aspects of the disclosure may be embodied in computer-usabledata or computer-executable instructions, such as in one or more programmodules, executed by one or more computers or other devices to performthe operations described herein. Generally, program modules includeroutines, programs, objects, components, data structures, and the likethat perform particular tasks or implement particular abstract datatypes when executed by one or more processors in a computer or otherdata processing device. The computer-executable instructions may bestored as computer-readable instructions on a computer-readable mediumsuch as a hard disk, optical disk, removable storage media, solid-statememory, RAM, and the like. The functionality of the program modules maybe combined or distributed as desired in various embodiments. Inaddition, the functionality may be embodied in whole or in part infirmware or hardware equivalents, such as integrated circuits,application-specific integrated circuits (ASICs), field programmablegate arrays (FPGA), and the like. Particular data structures may be usedto more effectively implement one or more aspects of the disclosure, andsuch data structures are contemplated to be within the scope of computerexecutable instructions and computer-usable data described herein.

Various aspects described herein may be embodied as a method, anapparatus, or as one or more computer-readable media storingcomputer-executable instructions. Accordingly, those aspects may takethe form of an entirely hardware embodiment, an entirely softwareembodiment, an entirely firmware embodiment, or an embodiment combiningsoftware, hardware, and firmware aspects in any combination. Inaddition, various signals representing data or events as describedherein may be transferred between a source and a destination in the formof light or electromagnetic waves traveling through signal-conductingmedia such as metal wires, optical fibers, or wireless transmissionmedia (e.g., air or space). In general, the one or morecomputer-readable media may be and/or include one or more non-transitorycomputer-readable media.

As described herein, the various methods and acts may be operativeacross one or more computing servers and one or more networks. Thefunctionality may be distributed in any manner, or may be located in asingle computing device (e.g., a server, a client computer, and thelike). For example, in alternative embodiments, one or more of thecomputing platforms discussed above may be combined into a singlecomputing platform, and the various functions of each computing platformmay be performed by the single computing platform. In such arrangements,any and/or all of the above-discussed communications between computingplatforms may correspond to data being accessed, moved, modified,updated, and/or otherwise used by the single computing platform.Additionally or alternatively, one or more of the computing platformsdiscussed above may be implemented in one or more virtual machines thatare provided by one or more physical computing devices. In sucharrangements, the various functions of each computing platform may beperformed by the one or more virtual machines, and any and/or all of theabove-discussed communications between computing platforms maycorrespond to data being accessed, moved, modified, updated, and/orotherwise used by the one or more virtual machines.

Aspects of the disclosure have been described in terms of illustrativeembodiments thereof. Numerous other embodiments, modifications, andvariations within the scope and spirit of the appended claims will occurto persons of ordinary skill in the art from a review of thisdisclosure. For example, one or more of the steps depicted in theillustrative figures may be performed in other than the recited order,and one or more depicted steps may be optional in accordance withaspects of the disclosure.

Extensions of the applications for this invention include the detectionof anomalous angle tilting on the camera which could indicate that thecamera may be loose or in a non-optimal position and thus needs to berepositioned or remounted (this can be sent as an alert to the driver).Another extension involves the application of the principles of distanceestimation provided for sports such as football where distances aremanually measured using to determine yardage. In such cases the marks onthe field can be used to determine camera parameters for any of thecameras pointing to the field and distance to objects can be determinedby the method presented in this invention, which will render manualmeasures unnecessary. Bicyclist can benefit from the technologypresented in this invention to exploit a camera mounted on their helmetso they can obtain speed and distance to objects information, as well asrelative speed and relative acceleration of other objects in thevicinity. Finally, the methods of this invention can be used to assistblind people by exploiting the information obtained from a body camerathat a blind person may carry. In this case when the blind personcrosses the street the pavement markings on the crossing provide thenecessary information to extract camera parameters and provide distanceinformation to the blind person about the distance to the other end ofthe street. In several cases while the blind person walks on the side ofthe street any pavement marking can be used to obtain an update of thecamera parameters and thus provide immediate distance information to theblind person for all the surrounding objects around.

What is claimed is:
 1. A computing platform, comprising: at least oneprocessor; a communication interface communicatively coupled to the atleast one processor; and memory storing computer-readable instructionsthat, when executed by the at least one processor, cause the computingplatform to: receive video footage from a vehicle camera, wherein thevehicle camera is located inside a vehicle, and wherein the videofootage includes a point of interest that is a longitudinal distance (D)in front of the vehicle camera and a horizontal distance (L) to a sideof the vehicle camera; determine, a height (H) of the vehicle cameracorresponding to a height of the vehicle camera above a ground plane, afocal length (fl) for the vehicle camera corresponding to a distancebetween a center of projection and an image plane for the vehiclecamera, and a vertical distance (d) between a middle point of the imageplane and an intersection point of the image plane and a line connectingthe center of projection and the point of interest on the ground plane;compute D based on H, fl, and d; determine a horizontal distance (w)between the middle point of the image plane and the intersection pointof the image plane and the line connecting the center of projection andthe point of interest on the ground plane; compute L based on w, D, andfl; compute, based on D and L, a distance between the vehicle and thepoint of interest; generate, based on the distance between the vehicleand the point of interest, driving metric output information and one ormore commands directing an accident analysis platform to cause displayof a driving metric output interface based on the driving metric outputinformation; and send, to the accident analysis platform, the drivingmetric output information and the one or more commands directing theaccident analysis platform to cause display of the driving metric outputinterface based on the driving metric output information, whereinsending the driving metric output information and the one or morecommands directing the accident analysis platform to cause display ofthe driving metric output interface based on the driving metric outputinformation causes the accident analysis platform to cause display ofthe driving metric output interface based on the driving metric outputinformation.
 2. The computing platform of claim 1, wherein determining Hcomprises determining h based on information received from a vehicleattribute database.
 3. The computing platform of claim 1, whereincomputing D comprises applying the following formula:$D = {\frac{{fl}*H}{d}.}$
 4. The computing platform of claim 1, whereincomputing L comprises applying the following formula:$L = {\frac{w*D}{fl}.}$
 5. The computing platform of claim 1, whereincomputing the distance between the vehicle and the point of interestcomprises computing √{square root over (D²+L²)}.
 6. The computingplatform of claim 1, wherein: the vehicle is in a first lane, and thepoint of interest is a second vehicle in a second lane.
 7. The computingplatform of claim 1, wherein the point of interest is located a distance(Z) above the ground plane.
 8. The computing platform of claim 7,wherein the memory stores additional computer-readable instructionsthat, when executed by the at least one processor, further cause thecomputing platform to: compute Z using the formula:${{H - Z} = \frac{r*D}{fl}},$ where r is a distance between a horizonline and an intersection between the image plane and a line connectingthe center of projection to the point of interest.
 9. The computingplatform of claim 8, wherein computing the distance between the vehicleand the point of interest comprises computing √{square root over(D²+L²+(H−Z)²)}.
 10. The computing platform of claim 1, wherein thememory stores additional computer-readable instructions that, whenexecuted by the at least one processor, further cause the computingplatform to: compute, using L, a lateral speed for the vehicle.
 11. Amethod comprising: at a computing platform comprising at least oneprocessor, a communication interface, and memory: receiving videofootage from a vehicle camera, wherein the vehicle camera is locatedinside a vehicle, and wherein the video footage includes a point ofinterest that is a longitudinal distance (D) in front of the vehiclecamera and a horizontal distance (L) to a side of the vehicle camera;determining, a height (H) of the vehicle camera corresponding to aheight of the vehicle camera above a ground plane, a focal length (fl)for the vehicle camera corresponding to a distance between a center ofprojection and an image plane for the vehicle camera, and a verticaldistance (d) between a middle point of the image plane and anintersection point of the image plane and a line connecting the centerof projection and the point of interest on the ground plane; computing Dusing the formula: ${D = {\frac{{fl}*H}{d}.}};$ determining a horizontaldistance (w) between the middle point of the image plane and theintersection point of the image plane and the line connecting the centerof projection and the point of interest on the ground plane; computing Lusing the formula: ${L = \frac{w*D}{fl}};$ computing a distance betweenthe vehicle and the point of interest by computing √{square root over(D²+L²)}; generating, based on the distance between the vehicle and thepoint of interest, driving metric output information and one or morecommands directing an accident analysis platform to cause display of adriving metric output interface based on the driving metric outputinformation; and sending, to the accident analysis platform, the drivingmetric output information and the one or more commands directing theaccident analysis platform to cause display of the driving metric outputinterface based on the driving metric output information, whereinsending the driving metric output information and the one or morecommands directing the accident analysis platform to cause display ofthe driving metric output interface based on the driving metric outputinformation causes the accident analysis platform to cause display ofthe driving metric output interface based on the driving metric outputinformation.
 12. The method of claim 11, wherein determining H comprisesdetermining h based on information received from a vehicle attributedatabase.
 13. The method of claim 11, wherein: the vehicle is in a firstlane, and the point of interest is a second vehicle in a second lane.14. The method of claim 11, wherein the point of interest is located adistance (Z) above the ground plane.
 15. The method of claim 14, furthercomprising: computing Z using the formula: ${{H - Z} = \frac{r*D}{fl}},$where r is a distance between a horizon line and an intersection betweenthe image plane and a line connecting the center of projection to thepoint of interest.
 16. The method of claim 15, wherein computing thedistance between the vehicle and the point of interest comprisescomputing √{square root over (D²+L²+(H−Z)²)}.
 17. The method of claim11, further comprising: computing, using L, a lateral speed for thevehicle.
 18. One or more non-transitory computer-readable media storinginstructions that, when executed by a computing platform comprising atleast one processor, a communication interface, and memory, cause thecomputing platform to: receive video footage from a vehicle camera,wherein the vehicle camera is located inside a vehicle, and wherein thevideo footage includes a point of interest that is a longitudinaldistance (D) in front of the vehicle camera and a horizontal distance(L) to a side of the vehicle camera; determine, a height (H) of thevehicle camera corresponding to a height of the vehicle camera above aground plane, a focal length (fl) for the vehicle camera correspondingto a distance between a center of projection and an image plane for thevehicle camera, and a vertical distance (d) between a middle point ofthe image plane and an intersection point of the image plane and a lineconnecting the center of projection and the point of interest on theground plane; compute D based on H, fl, and d; determine a horizontaldistance (w) between the middle point of the image plane and theintersection point of the image plane and the line connecting the centerof projection and the point of interest on the ground plane; compute Lbased on w, D, and fl; compute, based on D and L, a distance between thevehicle and the point of interest; computing, using L, a lateral speedfor the vehicle; generate, based on the distance between the vehicle andthe point of interest, driving metric output information and one or morecommands directing an accident analysis platform to cause display of adriving metric output interface based on the driving metric outputinformation, wherein the driving metric output information includes thelateral speed and the distance between the vehicle and the point ofinterest; and send, to the accident analysis platform, the drivingmetric output information and the one or more commands directing theaccident analysis platform to cause display of the driving metric outputinterface based on the driving metric output information, whereinsending the driving metric output information and the one or morecommands directing the accident analysis platform to cause display ofthe driving metric output interface based on the driving metric outputinformation causes the accident analysis platform to cause display ofthe driving metric output interface based on the driving metric outputinformation.
 19. The one or more non-transitory computer-readable mediaof claim 18, wherein determining H comprises determining h based oninformation received from a vehicle attribute database.